Health Monitoring Of Civil Infrastructures Research Articles

3D printed self-sensing cementitious composites using graphite and carbon microfibers

Cementitious materials possessing self-sensing properties conferred through the incorporation of conductive fillers have attracted significant interest in the research community due to their potential application to structural health monitoring of civil infrastructure. A key advantage of cementitious sensors is that they can be organically embedded within cementitious structures to provide extremely robust monitoring capabilities. Of interest to this paper, the integration of cementitious sensors could be critical in empowering the 3D printing of cementitious composites by enabling real-time monitoring of additive manufacturing. Such integration could be achieved by 3D printing self-sensing cementitious nodes during the fabrication process. 3D printed self-sensing cementitious sensors could also be topologically engineered to achieve new opportunities in sensing of existing and new concrete and other cementitious structures. This paper presents one of the earliest studies, if not the first, on the 3D printing of self-sensing functionalized composites. The self-sensing cementitious composite sensors are fabricated using a cementitious matrix doped with graphite (G) and carbon microfibers. Various mixtures are studied, in particular G-only, G with milled carbon microfibers (MCMF), G with chopped carbon microfibers (CCMF), and G with MCMF and CCMF. A study of the percolation curves reveals that it is possible to attain electrical percolation using these conductive particles to create strain-sensitive sensors by leveraging the boosted piezoresistive effect. A study of strain sensing performance conducted on various specimens fabricated using different mixture types reveals the optimal concentration of dopants under different types of conductive particles. It is found that the optimal mixture that yields optimal performance also improves the Young’s Modulus of the specimens by 42.2% compared to cement-only specimens. Overall, this optimal mixture composed of 10% weight-to-cement G (10 wt% G), 0.250 wt% MCMF, and 0.125 wt% CCMF yielded a static gauge factor of 622, a resolution of 167 με , an accuracy of 19.24 με , and a Young’s Modulus of 798 MPa.

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures.

The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

Detection of cracks in concrete using near-IR fluorescence imaging

Structural health monitoring of civil infrastructure is a crucial component of assuring the serviceability and integrity of the built environment. A primary material used in the construction of civil infrastructure is concrete, a material that is susceptible to cracking due to a variety of causes, such as shrinkage, creep, overloading, and temperature change. Cracking reduces the durability of concrete structures, as it allows deleterious environmental agents to penetrate the surface, causing such damage as corrosion of steel reinforcement and delamination of the concrete itself. Conventional crack detection techniques are limited in scope due to issues relating to pre-planning, accessibility, and the need for close proximity to the test surface. Contactless optical image monitoring techniques offer the opportunity to overcome these limitations and have the potential to detect cracks at a distance. Concrete has been reported to have a near-infrared (Near-IR) fluorescence line at a wavelength of 1140 nm when excited with red light. This work investigates the use of fluorescence imaging for the detection of cracks in cementitious surfaces using shallow angle incidence excitation red light. Light oriented at a shallow angle does not excite interior surfaces of cracks, which appear as darker features in images of fluorescing concrete. Artificial cracks with widths of 0.2–1.5 mm were readily imaged using a near-IR camera at distances of 0.5 and 1.3 m. An additional concrete sample with a 0.08 mm wide crack was produced using a flexure apparatus and was also imaged. It is worth noting that the 0.08 mm crack was detected despite its width being below the 0.1 mm pixel resolution of the camera, with the aid of digital image enhancement algorithms.

On the use of domain adaptation techniques for bridge damage detection in a changing environment

AbstractStructural Health Monitoring of civil infrastructures often suffers from the limited availability of damage labelled data. The work here seeks to overcome this issue by using Transfer Learning approaches, in the form of Domain Adaptation, for leveraging information from a source structure with determined health‐state labels to make inferences on an unlabeled monitored structure. The idea is to exploit source data to train a Machine Learning algorithm and achieve improved early‐stage damage detection capabilities across a population of bridges. To account for differences in the underlying distributions of each structure, Transfer Learning is seen as a strategy enabling population‐level bridge SHM. In this paper, the natural frequencies obtained from multiple vibration measurements are extracted to characterise different domains during pristine and abnormal conditions. Such damage‐sensitive features are aligned via Domain Adaptation and used to train a standard classifier within a shared feature space. The methodology is validated on the heterogeneous population composed of the Z24 and S101 bridges. The results prove the effectiveness to successfully exchange damage labels, thus increasing available information for health‐state inference for SHM applications with sparce datasets.

A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals

Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure’s health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge’s health status. In particular, this algorithm involves the training of N models, each of them able to estimate the quantity measured via a sensor by using the others’ N−1 measurements. Hence, the system can be represented by the ensemble of the N models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness.

Ring-core few-mode fiber and DPP-BOTDA-based distributed large-curvature sensing eligible for shape reconstruction.

This study proposes a distributed large-curvature sensor based on ring-core few-mode fiber (RC-FMF) and differential pulse-pair Brillouin optical time-domain analysis (DPP-BOTDA). The RC-FMF is adhered to a thin steel substrate and an asymmetric hump shape is reconstructed using the Frenet-Serret algorithm. The proposed curvature sensor demonstrates a larger curvature-sensing range, excellent tolerance to bending-induced optical loss, and increased Brillouin gain coefficient. The proposed sensor also demonstrates longer sensing distance and continuous absolute measurement compared to other sensors. The proposed model can be applied to the end tracking of soft robotics and structural health monitoring of civil infrastructures.

Artificial intelligence in civil infrastructure health monitoring—Historical perspectives, current trends, and future visions

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research.

Numerical investigation of a bioinspired multi-segment soft pneumatic actuator for grasping applications

Soft robotic gripper is useful for grasping applications, such as pick-and-place tasks, post-disaster rescue and health monitoring of civil infrastructure. Inspired by the versatility of the human hand and bird claws, this paper designed a bioinspired actuator made of multi-segment fibre-reinforced pneumatic bending actuator and bellow-shape pneumatic rotational actuator joints. A numerical model was established to investigate the bending performance and stress distribution of the designed actuator under different input pressures. The influences of bottom layer thickness, chamber number at the actuator joints, and bending segment length on the overall bending performance of the designed soft actuator have been comprehensively investigated. In addition, the stress distribution of the actuator joints has been studied for understanding the mechanical performance of the actuator joints. Finally, a soft gripper made of two designed actuators has been demonstrated for grasping objects with different shapes and weights. The findings found in this investigation are helpful for the design and fabrication of robotic grasper, which can be further integrated on an industrial robotic arm and flying robotic platform for grasping and prosthetic hand applications.

A Review Paper on Importance of SHM System in India

Abstract: India being the home of rich historical background inherits varied amount of historical structures like Taj Mahal (one of the seven wonders), Red fort, Temples etc. Due to their historical importance, it becomes very important to assess health condition of these structures and take the required measures to improve their life. Buildings like stadiums, sports arenas, malls, lifeline structures like hospitals, public buildings which could cause harm to large number of people at a time and are something to be taken care on a regular basis. India also has various ambitious, complex, and critical structure, like nuclear plants, Dams, bridges and tunnels should be mandated with monitoring as their failure cause more losses than any other. Structural health monitoring will help us to achieve increased durability and health of structure, reduced maintenance and improved safety. Health monitoring of civil infrastructures consists of determining by measured parameter, the location and severity of damage in structures and inform us before it became to late. The main goal of SHM is to detect the damage based on the measurement by various types of sensors, locate the geometric position of the damage and quantify the damage so that we can take the necessary action timely and improve the life of the structure. Keywords: Bridge, Structural Health Monitoring (SHM), sensors, data management system, instrumentation scheme, Performance parameters

Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring

Real-time health monitoring of civil infrastructures is performed to maintain their structural integrity, sustainability, and serviceability for a longer time. With smart electronics and packaging technology, large amounts of complex monitoring data are generated, requiring sophisticated artificial intelligence (AI) techniques for their processing. With the advancement of technology, more complex AI models have been applied, from simple models to sophisticated deep learning (DL) models, for structural health monitoring (SHM). In this article, a comprehensive review is performed, primarily on the applications of AI models for SHM to maintain the sustainability of diverse civil infrastructures. Three smart data capturing methods of SHM, namely, camera-based, smartphone-based, and unmanned aerial vehicle (UAV)-based methods, are also discussed, having made the utilization of intelligent paradigms easier. UAV is found to be the most promising smart data acquisition technology, whereas convolution neural networks are the most impressive DL model reported for SHM. Furthermore, current challenges and future perspectives of AI-based SHM systems are also described separately. Moreover, the Internet of Things (IoT) and smart city concepts are explained to elaborate on the contributions of intelligent SHM systems. The integration of SHM with IoT and cloud-based computing is leading us towards the evolution of future smart cities.

Use of Micro-Electrochemical Systems-based Technologies for Civil Infrastructure Health Monitoring

Use of Micro-Electrochemical Systems-based Technologies for Civil Infrastructure Health Monitoring

Curve‐based crack detection using crack information gain

Crack detection provides valuable information concerning the location, extent, type, and severity for structural health monitoring of civil infrastructures. Due to the influence of uneven lighting and imaging noise, as well as the debris, various textures, and materials of the different civil structure, detecting cracks in the surface images of civil infrastructures remains challenging in that there are many false positives and defragmentation of the crack curve. In this paper, an information-based crack detection method is developed to robustly characterize and detect cracks on a curve-by-curve basis. Crack information gain (CIG) is defined to characterize the information of a local patch's being cracked. With the proposed model of estimating crack depth, the information-based crack descriptor CIG is calculated by statistically modeling the probabilistic distribution of crack depth. Secondly, crack curves of different saliency are progressively detected by iteratively searching the salient string of pixels, regardless of the small gaps between fragments of the crack curve. Finally, crack curves are validated by examining the variation of CIG along the crack curve. The experimental results on a diverse set of images of different civil infrastructures demonstrate the generalizability of the proposed method, and the overall performance on each dataset outperforms the state-of-the-art available methods with 2.9% improvement. The proposed method has potential for the quantitative evaluation of cracks on a meaningful curve basis.

Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

This paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

Starting from the early years of the 21st century, the problem of monitoring the physical–chemical–mechanical conditions of structures and infrastructures for civil use began to be thought in a significantly different way than in the last century [...]

Identification of modal strains in concrete beams at sub-microstrain amplitude excitation using fibre Bragg grating sensors mounted on a strain-amplifying transducer

Vibration-based damage identification is a non-destructive method that enables the health monitoring of civil infrastructures. It aims to detect the presence and location of damage by measuring changes in the vibration characteristics of these structures. Unfortunately, the most popular vibrational parameters – natural frequencies and modal displacements – have a low sensitivity to certain types of local damage. Modal strains and curvatures, on the other hand, can be sufficiently sensitive to local damage, but monitoring modal strains is challenging. Indeed, the strain amplitudes can be in the sub-microstrain range (<1 με) when considering ambient excitation which is too small for most conventional techniques. Here we show that such measurements can be successfully carried out in a quasi-distributed manner with fibre Bragg grating–based sensors that have been mounted on a dedicated strain-amplifying transducer. First, we report on lab-scale dynamic tests on a 5-m-long concrete beam, equipped with such transducers having a strain amplification of 62. Our results show that we can identify the first three bending modes while the average strain level on the beam was only 0.06 με. Second, we present the first field test for these transducers conducted on a high-speed railway viaduct. We have succeeded to obtain six strain modes of the viaduct from the data collected with fibre Bragg gratings on the transducers at an average strain level of 0.067 με. To the best of our knowledge, this is the first time that strain mode identification in operational conditions using strain-amplifying transducers was successful. This demonstration can be a starting point for the implementation of vibration-based damage identification in civil structures allowing the fulfilment of its long-standing promise.

Recent Progress of Fiber-Optic Sensors for the Structural Health Monitoring of Civil Infrastructure.

In recent years, with the development of materials science and architectural art, ensuring the safety of modern buildings is the top priority while they are developing toward higher, lighter, and more unique trends. Structural health monitoring (SHM) is currently an extremely effective and vital safeguard measure. Because of the fiber-optic sensor’s (FOS) inherent distinctive advantages (such as small size, lightweight, immunity to electromagnetic interference (EMI) and corrosion, and embedding capability), a significant number of innovative sensing systems have been exploited in the civil engineering for SHM used in projects (including buildings, bridges, tunnels, etc.). The purpose of this review article is devoted to presenting a summary of the basic principles of various fiber-optic sensors, classification and principles of FOS, typical and functional fiber-optic sensors (FOSs), and the practical application status of the FOS technology in SHM of civil infrastructure.

Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders

Damage detection is one of the most important tasks for structural health monitoring of civil infrastructure. Before a damage detection algorithm can be applied, the integrity of the data must be ensured; otherwise results may be misleading or incorrect. Indeed, sensor system malfunction, which results in anomalous data (often called faulty data), is a serious problem, as the sensors usually must operate in extremely harsh environments. Identifying and eliminating anomalies in the data is crucial to ensuring that reliable monitoring results can be achieved. Because of the vast amounts of data typically collected by a structural health monitoring system, manual removal of the anomalous data is prohibitive. Machine learning methods have the potential to automate the process of data anomaly detection. Although supervised methods have been proven to be effective for detecting data anomalies, two unresolved challenges reduce the accuracy of anomaly detection: (1) the class imbalance and (2) incompleteness of anomalous patterns of training dataset. Unsupervised methods have the potential to address these challenges, but improvements are required to deal with vast amounts of monitoring data. In this article, the generative adversarial networks are combined with a widely applied unsupervised method, that is, autoencoders, to improve the performance of existing unsupervised learning methods. In addition, the time-series data are transformed to Gramian Angular Field images so that advanced computer vision methods can be included in the network. Two structural health monitoring datasets from a full-scale bridge, including examples of anomalous data caused by sensor system malfunctions, are utilized to validate the proposed methodology. Results show that the proposed methodology can successfully identify data anomalies with good accuracy and robustness, hence can overcome one of the key difficulties in achieving automated structural health monitoring.

Health Monitoring of Civil Infrastructures by Subspace System Identification Method: An Overview

Structural health monitoring (SHM) is the main contributor of the future’s smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author’s knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated.

Structural modal identification and health monitoring of building structures using self-sensing cementitious composites

Recently self-sensing cementitious composite has demonstrated its strong potentiality for structural health monitoring of civil infrastructures because of its low-cost, long-term stability and compatibility with concrete structures. In this paper, we propose novel hybrid nanocarbon materials engineered cement-based sensors (HNCSs) with high-sensitivity, which are fabricated with self-sensing cementitious composites containing electrostatic self-assembled CNT/NCB composite fillers. The mechanical property and sensing performance of the HNCSs are pre-characterized under static and dynamic compressive loadings. The HNCSs are then integrated into a five-story building model via custom-made clamps to verify the feasibility for dynamic response measurements. Results show that the developed sensors have satisfactory mechanical property and excellent pressure-sensitive reproducibility and stability. With clamps holding on the building model, the HNCSs perform satisfactorily under sinusoidal excitations in the frequency range from 2 to 40 Hz. In addition, the modal frequencies and their changes of the building model caused by ‘damage’ simulated through adding additional masses identified by the HNCSs are favorably consistent with the counterparts acquired by accelerometers and strain gauges, indicating that the developed HNCSs have great potential for structural modal identification and damage detection applications.

A Machine Learning Approach for Structural Health Monitoring Using Noisy Data Sets

Continuous structural health monitoring of civil infrastructure can be achieved by deploying an Internet of Things network of distributed acceleration sensors in buildings to capture floor movement. Postdisaster damage levels can be computed based on the peak relative floor displacement as specified in government standards. This article uses machine learning approaches to identify the status of buildings postevent based on accelerometer traces. Prior work in the field assumed the use of high-quality accelerometers for displacement estimation. In this article, we focus on using lower quality and cheaper accelerometers, while accounting for noise effects by incorporating noisy data sets in machine learning approaches for classification. A labeled acceleration data set of buildings response to earthquakes was created, where each sample is labeled with its corresponding damage severity. Sensor noise is included in the data set to model nonideal sensors. Classification performance of machine learning algorithms, such as support vector machine, K-nearest neighbor, and convolutional neural network, is presented. Techniques for addressing noise levels are proposed, and the results are compared with regular noise cancellation techniques that adopt high-pass filtering. Note to Practitioners —This article presents a methodology for automatic estimation of buildings status in the aftermath of a natural disaster, such as an earthquake. It focuses on using low-cost inertial sensors, such as accelerometers, to sense buildings’ vibrations and then applying machine learning algorithms to detect damage. Utilizing the convolutional network approach, the proposed methods detect the building damage state with high accuracy. Since this article focuses on using cheap sensors, the cost of deploying a sensor network to monitor buildings is reduced significantly. Deploying this network enables rescue and reconnaissance teams to have a clear view of the most vulnerable structures.